eCommerce Analytics

Though Kevin Hillstrom believes that the low variable cost is the real factor in the “killer ROI” that email affords multi-channel retailers, I would also mention that it’s low variable cost also contributes to the low amount of effort that retailers put into their email marketing. That is, retailers don’t put the money they should behind email because they view the low variable cost - and low marginal cost - as a license to spam. The way this manifests itself in the marketplace is that spending on email optimization technology trudges behind other technological spending like, say, search engine optimization (SE0).

Read Targeted Email Campaigns Only Way to Recapture Email ROI »

One question that we’re repeatedly asked by our customers is how they compare to the “average” online marketer or retailer in terms of conversion rates, number of campaigns, customer acquisition, etc.  Many times it’s hard to pinpoint exact numbers with much accuracy because of differences in business model and marketing channels, but a new survey that just came out on eMarketer gives a fairly nice overview of how current online retailers (limited to the US) are doing in regards to email marketing.

Read Better than average? »

Marketing Sherpa put out the 2009 Email Marketing Benchmark Guide today. One of the insights from the report is that email, in a down economy, is the comfort food that direct marketers curl up with next to the fire. That is, retailers will depend on it more than ever. At Istobe, we’re not surprised. Email marketing, after all, is a mature medium where smart retailers can meaningfully personalize content at a low cost.

Read Marketing Sherpa Says Importance of Targeted Email Will Increase During Economic Downturn »

Many customers have asked us to help them better understand the effect marketing messages have on their customer base. Almost everyone we know uses multi-channel marketing in one form or another - whether it’s email and web ads or email, web ads, and direct mail - most companies are using more than one medium to get their message out to new and existing customers. The problem many companies have is determining how and when to target each customer with the appropriate message.

Read Multi-Channel Marketing and the Zone of Influence »

While working on a proposal the other day for a prospective customer, I decided that I’d go the extra length for him in an attempt to demonstrate where exactly the company could make up some ground in its effort to realize a bit more bang for its buck in its email marketing program. That is, the company wanted to make more money from its existing customer base. When I looked at the company’s email marketing statistics, I was surprised to find that their clickthroughs per purchase was much higher than any company I’d seen.

Read Clickthroughs Per Purchase is the Gold Standard for Targeted Email »

The blogosphere is awash in tips for doing email targeting.  Some are good (almost anything advocating testing email performance) but some are just flat out wrong.  Here are three commonly held tenets of email targeting that you should ignore.

Read 3 Email Targeting Myths »

Just finished getting through the backlog of my Don Dodge RSS feed today and I’m happy to report that venture capitalists seems to think that businesses like Istobe are about to break out. Let me qualify that. Venture capitalists seem to think that using data to improve e-commerce is an industry that clearly needs some maturing and that maturing time is nigh. Istobe represents that maturing, combining hundreds of models and data integration routines into a package that lets you target the right customer with the right product at the right time.

Investors believe that the maturation in this industry will occur in the next five years. Well, so does Istobe. We believe that it’s time to put your data to use. If you don’t use it, data is no more than the new shelfware: that software you just had to have before you realized you lacked the in-house talent to unlock its value. Istobe is your outsourced in-house data analysis talent that lets you ask simple questions and get answers without analysis, questions like: I need to sell this overstock of shirts, to whom should I market them? In reply, Istobe gives you a list of your customers that are most likely to buy your shirts and the probability that they will buy. This is the new paradigm in predictive modeling that Gartner calls the data mining packaged application. Let’s just take a quick look at the moment in time at which we are poised.

Data collection methods are clearly refined

Nowadays, everyone is sitting on a pile of customer data that they don’t know what to do with. As Rob Hayes, partner at First Round Capital, says in the Stefanie Olsen article from which Dodge draws his inspiration, “Everyone talks about all the data that’s being created and how valuable it is, but the way you make it available is by doing something actionable with it.” The glut of data is due, in part, to years of CRM implementations and the current CRM zeitgeist. You can’t turn around without being inundated by a flood of marketing for “next generation” CRM systems. In fact, I used to be one of those marketers at a not-too-small company that builds Dynamics CRM.

Of course, online everything has made data more prevalent as well. Returning some kind - any kind - of functionality in exchange for your profile is no longer new hat. Every widget and social network known to man requires you to divulge information before you start using it. And then it collects your clickstream as you use the app. Heck, I’ve got at least 50 different login/pass pairs that I need to remember now.

Purchases, as well, fall into this category. With online shopping increasing at a terrifying clip, all of your purchases are more seamlessly collected and tied to your profile and your clickstream, meaning that now, more than ever; pre-purchase behavior and purchaser characteristics - on a large scale - are at a marketer’s fingertips.

Data analysis tools have matured but haven’t turned the corner

So there are various types of data out there right now that need tying together and, ultimately, analysis. But have the tools to merge and make sense of that data improved? Not appreciably. Really, when you get right down to it, the tools used to perform data analysis are still catch-all tools that can build any model you want or merge any type of data you want. But they can’t help you with specific business problems. In other words, the tools exist for database experts (data integration) and PhD statisticians (statistical modeling tools).

For years, these tools have gotten easier for experts to use but haven’t gotten any easier for business users. This means that your $300K worth of in-house data integrators and PhD statisticians have become slightly more productive over time but translating their language into the language of business is as difficult as ever. And turning the data they spit out into a meaningful business strategy is just as tough.

With today’s news that the retail sector is experiencing a slowdown, now is a better time than ever for multi-channel retailers to do two things: turn to cheaper forms of advertising (email) and use quick-return customer analytics to compete with gargantuan discounters like Wal-Mart that threaten to swallow retail whole. The truth is that Wal-Mart will continue to invest in analytics during the tough economy because they will see immediate ROI from understanding which customers are poised to buy, which items they want, and how much those customers are willing to spend. I can think of two, good reasons for smaller multi-channel retailers to follow suit.

Harvest your current customers
Most would say that the thick of a poor economy is a poor time to invest in new marketing projects. If these projects are tied to new customer acquisition, I might agree. It’s damned expensive to acquire customers and you tend to forget what you already have while you’re out prospecting, buying lists, etc. Sometimes, the answer is in front of you. In a poor economy, isn’t it imperative that you retreat to your base? Multi-channel retailers need to figure out ways to:

A. Not lose your current customers to competition (like Wal-Mart)
B. Harvest your existing customers by making them feel as though you understand them

Really, achieving B is the answer to question A. A redoubling of your customer service effort will always make your customers more loyal and less likely to jump ship. But we have to remember that larger players can always offer deeper discounts in an effort to combat your superior customer understanding. One way around this is to deepen your customer understanding on the marketing front with timely, personalized emails to your customer base. Ultimately, if you can address your customers’ needs first - make your customers offers at the cusp of when they need those products - then you are likely to win their business. This is the advantage that predictive models based on your customers behaviors provide you: the ability to beat your larger competition on timing as opposed to discounting.

Quick ROI
Customer analytics like those that Istobe proposes are great because the analysis takes advantage of data that you, as a multi-channel retailer, already possess. You’ve already got a record of your cusotmers’ purchases. In other words, there is no up-front infrastructure or talent investment. What this ultimately means is that your ROI emerges quickly. How quick? Well, let’s just say that you’re in the black (or, green) around month two. This is especially true if you’re already used to sending your customer data to a co-op database (like Abacus or NextAction); you’ve already made your data collection and transfer investment. Now it’s simply about turning those investments to a different use - customer development not acquisition - by focusing how that data helps you pull in the monetary margins in your current customer base.

These days even the most technophobic consumers have inboxes full of marketing from companies they have interacted with. As responsible marketers, we have ensured that these customers have opted in to our communications and we know that we must promptly remove them from our house file when they no longer want to hear from us. However, according to Marketing Sherpa’s Email Marketing Benchmark Guide 2008 (summary here), ensuring opt-in may no longer be enough to keep our company’s image clean.

In a survey of over 4000 consumers, half consider email to be spam if it arrives too frequently, even if it comes from a known sender. This has serious consequences for email marketers using “carpet-bombing” strategies to spur customers to purchase. Even if consumers have opted in and know a company well, they may come to think it as a spammer if they are receiving marketing emails every day or every week.

The sentiment that, regardless of permission, frequent email marketing is spam will only grow as inboxes become even more flooded. Marketers will be forced to migrate to a “surgical-strike” strategy where customers are targeted with highly personalized messages only at the most likely time to buy, and probably no more than once a month.

In an environment where consumer trust is hard to gain and can vanish with one misstep, nobody wants to be seen as a spammer. Unfortunately, the risk of marketing too frequently is now beginning to outweigh the benefit. If email marketers do not adapt through better targeting, they may find themselves relegated to the junk folder for good.

I noticed that the RRW Consulting blog alluded to an article on Friday that I have been promoting to my peers: a research report by the Aberdeen Group (abstract here) that discusses the importance of email personalization. The one-to-one marketing emphasis in the article is precisely the kind of email targeting that we espouse here at Istobe. Today, I want to expand on one aspect of the Aberdeen report that we spend extra time on at Istobe: the importance of the buying cycle in determining what kind of email message to send your customers.

In the Aberdeen article, Ian Michiels mentions that web analytics provide great clues to assessing where customers are in the buying cycle. For example, if a customer invests a vast amount of time clicking about a product group, that customer is likely doing research and is in the market to buy a product in that area. A discount offer, Michiels says, would likely get this customer - who is now highly qualified and advanced in the buying cycle - to act on their desire and make a purchase.

I totally agree with this sentiment. But as Chris mentioned in detailing his experience with GPS systems at Amazon, there is another way to do this. Customers can clue you into what they want via their clickstream. But even if you don’t have clickstream data, transaction histories, once supercrunched, can give you a leg up on finding customers who will likely buy next. In other words, this supercrunching can help you locate the customers that will likely buy before they locate you.

How does this work? Well, other customers have come before them and laid out patterns that aren’t perceptible to you and I but are very perceptible to Istobe’s predictive models. Istobe’s models throw out those customers that are not likely to buy again and then work with those who are. From there, Istobe’s models assign the products that are likely to be purchased by these likely buyers.

I won’t argue that this method is more statistically powerful than clickstream data, which is a solid indicator of future behavior. But I will argue that clickstream data takes vast amounts of resources to capture and use, a difficult proposition for online retailers who are just dipping their toes into analytics. And using transactional data to predict who will buy next is a more proactive approach. So what do you get from that proactivity? Probably a two- to three-month head start on your competition. You can focus on targeting your “most likely” customers with act-now offers while your competition waits for these customers to visit their web site.